Image classification of artificial fingerprints using Gabor wavelet filters, self-organising maps and Hermite/Laguerre neural networks

Abstract

Image classification was performed using Gabor wavelet filters for image feature extraction, self-organising maps (SOM) for dimensional reduction of Gabor wavelet filters, and forward (FNN), Hermite (HNN) and Laguerre (LNN) neural networks to classify real and artificial fingerprint images from optical coherence tomography (OCT). Use of a SOM after Gabor edge detection of OCT images of fingerprint and material surfaces resulted in the greatest classification performance when compared with moments based on colour, texture and shape. The FNN and HNN performed similarly, however, the LNN performed the worst at a low number of hidden nodes but overtook performance of the FNN and HNN as the number of hidden nodes approached n = 10.